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NASA NTRSDate not supplied

Demonstration of Lidar Sensors for Precision Safe Landing on Planetary Bodies

Missions to solar system bodies must meet increasingly ambitious objectives requiring highly reliable “precision landing”, and “hazard avoidance” capabilities. To meet these needs, we have developed two lidar sensor systems that can be used individually or in concert depending on the mission requirements. One is Navigation Doppler Lidar (NDL) capable of providing vehicle precision vector velocity and ground-relative altitude and the other is an Imaging Flash Lidar for Terrain Relative Navigation and Hazard Detection and Avoidance. Using both lidar sensors together will enable landing anywhere and under any lighting condition. The NDL performance has been extensively characterized through numerous ground and aircraft flight tests and will be soon demonstrated on two lunar landing missions (mid-2023). The Flash Lidar performance is currently being assessed onboard a drone and will soon be tested on helicopter and fixed-wing aircraft platforms. This paper describes both lidar sensors and their expected operation on landing vehicles.

Precision Landing↗
NASA NTRS2016

Laser Transmitter Design and Performance for the Slope Imaging Multi-Polarization Photon-Counting Lidar (SIMPL) Instrument

The Slope Imaging Multi-polarization Photon-counting Lidar (SIMPL) instrument is a polarimetric, two-color, multibeam push broom laser altimeter developed through the NASA Earth Science Technology Office Instrument Incubator Program and has been flown successfully on multiple airborne platforms since 2008. In this talk we will discuss the laser transmitter performance and present recent science data collected over the Greenland ice sheet and sea ice in support of the NASA Ice Cloud and land Elevation Satellite 2 (ICESat-2) mission to be launched in 2017.

Yu, Anthony W.↗
NASA NTRS1990

Cirrus structure and radiative parameters from airborne lidar and spectral radiometer observations - The 28 October 1986 FIRE study

A description is presented of cirrus based on results from a FIRE observation flight in central Wisconsin on October 28, 1986. Cirrus structure and radiative parameters as determined by the ER-2 lidar and imaging spectral radiometers are presented. From the lidar observations a complex structure was shown with differing cloud layers extending over six kilometers of altitude range. Both thin and dense cirrus layers were present and mixed phase clouds were found at lower altitudes. As indicated by the cloud structure, precipitation of crystals from high, but vertically thin, layers produces a significant fraction of the lower cirrus. Multiple layers should be considered as normal for cirrus formations. It is noted that the cloud height is an important factor for satellite cloud retrievals and cloud climatology.

Spinhirne, James D.↗
NASA NTRS2011

Measuring Effective Leaf Area Index, Foliage Profile, and Stand Height in New England Forest Stands Using a Full-Waveform Ground-Based Lidar

Effective leaf area index (LAI) retrievals from a scanning, ground-based, near-infrared (1064 nm) lidar that digitizes the full return waveform, the Echidna Validation Instrument (EVI), are in good agreement with those obtained from both hemispherical photography and the Li-Cor LAI-2000 Plant Canopy Analyzer. We conducted trials at 28 plots within six stands of hardwoods and conifers of varying height and stocking densities at Harvard Forest, Massachusetts, Bartlett Experimental Forest, New Hampshire, and Howland Experimental Forest, Maine, in July 2007. Effective LAI values retrieved by four methods, which ranged from 3.42 to 5.25 depending on the site and method, were not significantly different ( b0.1 among four methods). The LAI values also matched published values well. Foliage profiles (leaf area with height) retrieved from the lidar scans, although not independently validated, were consistent with stand structure as observed and as measured by conventional methods. Canopy mean top height, as determined from the foliage profiles, deviated from mean RH100 values obtained from the Lidar Vegetation Imaging Sensor (LVIS) airborne large-footprint lidar system at 27 plots by .0.91 m with RMSE=2.04 m, documenting the ability of the EVI to retrieve stand height. The Echidna Validation Instrument is the first realization of the Echidna lidar concept, devised by Australia's Commonwealth Scientific and Industrial Research Organization (CSIRO), for measuring forest structure using full-waveform, ground-based, scanning lidar.

Zhao, Feng↗
NASA NTRS1992

Fluorescence lidar multi-color imaging of vegetation

Multi-color imaging of vegetation fluorescence following laser excitation is reported for distances of 50 m. A mobile laser radar system equipped with a Nd:YAG laser transmitter and a 40 cm diameter telescope was used. Image processing allows extraction of information related to the physiological status of the vegetation and might prove useful in forest decline research.

Johansson, J.↗
NASA NTRS2014

Methods of Real Time Image Enhancement of Flash LIDAR Data and Navigating a Vehicle Using Flash LIDAR Data

A method for creating a digital elevation map ("DEM") from frames of flash LIDAR data includes generating a first distance R(sub i) from a first detector i to a first point on a surface S(sub i). After defining a map with a mesh THETA having cells k, a first array S(k), a second array M(k), and a third array D(k) are initialized. The first array corresponds to the surface, the second array corresponds to the elevation map, and the third array D(k) receives an output for the DEM. The surface is projected onto the mesh THETA, so that a second distance R(sub k) from a second point on the mesh THETA to the detector can be found. From this, a height may be calculated, which permits the generation of a digital elevation map. Also, using sequential frames of flash LIDAR data, vehicle control is possible using an offset between successive frames.

Vanek, Michael D.↗
DOE OSTI2020

Shallow cumuli cover and its uncertainties from ground-based lidar–radar data and sky images

Cloud cover estimates of single-layer shallow cumuli obtained from narrow field-of-view (FOV) lidar–radar and wide-FOV total sky imager (TSI) data are compared over an extended period (2000–2017 summers) at the established United States Atmospheric Radiation Measurement mid-continental Southern Great Plains site. We quantify the impacts of two factors on hourly and sub-hourly cloud cover estimates: (1) instrument-dependent cloud detection and data merging criteria and (2) FOV configuration. Enhanced observations at this site combine the advantages of the ceilometer, micropulse lidar (MPL) and cloud radar in merged data products. Data collected by these three instruments are used to calculate narrow-FOV cloud fraction (CF) as a temporal fraction of cloudy returns within a given period. Sky images provided by TSI are used to calculate the wide-FOV fractional sky cover (FSC) as a fraction of cloudy pixels within a given image. To assess the impact of the first factor on CF obtained from the merged data products, we consider two additional subperiods (2000–2010 and 2011–2017 summers) that mark significant instrumentation and algorithmic advances in the cloud detection and data merging. We demonstrate that CF obtained from ceilometer data alone and FSC obtained from sky images provide the most similar and consistent cloud cover estimates; hourly bias and root-mean-square difference (RMSD) are within 0.04 and 0.12, respectively. However, CF from merged MPL–ceilometer data provides the largest estimates of the multiyear mean cloud cover, about 0.12 (35 %) and 0.08 (24 %) greater than FSC for the first and second subperiods, respectively. CF from merged ceilometer–MPL–radar data has the strongest subperiod dependence with a bias of 0.08 (24 %) compared to FSC for the first subperiod and shows no bias for the second subperiod. The strong period dependence of CF obtained from the combined ceilometer–MPL–radar data is likely results from a change in what sensors are relied on to detect clouds below 3 km. After 2011, the MPL stopped being used for cloud top height detection below 3 km, leaving the radar as the only sensor used in cloud top height retrievals. To quantify the FOV impact, a narrow-FOV FSC is derived from the TSI images. We demonstrate that FOV configuration does not modify the bias but impacts the RMSD (0.1 hourly, 0.15 sub-hourly). In particular, the FOV impact is significant for sub-hourly observations, where 41 % of narrow- and wide-FOV FSC differ by more than 0.1. A new “quick-look” tool is introduced to visualize impacts of these two factors through integration of CF and FSC data with novel TSI-based images of the spatial variability in cloud cover. The influence of cloud field organization, such cloud streets parallel to the wind direction, on narrow- and wide-FOV cloud cover estimates can be visually assessed.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Clementine: Anticipated scientific datasets from the Moon and Geographos

The Clementine spacecraft mission is designed to test the performance of new lightweight and low-power detectors developed at the Lawrence Livermore National Laboratory (LLNL) for the Strategic Defense Initiative Office (SDIO). A secondary objective of the mission is to acquire useful scientific data, principally of the Moon and the near-Earth asteroid Geographos. The spacecraft will be in an elliptical polar orbit about the Moon for about 2 months beginning in February of 1994 and it will fly by Geographos on August 31. Clementine will carry seven detectors each weighing less than about 1 kg: two Star Trackers wide-angle uv/vis wide-angle Short Wavelength IR (SWIR) Long-Wavelength IR (LWIR) and LIDAR (Laser Image Detection And Ranging) narrow-angle imaging and ranging. Additional presentations about the mission detectors and related science issues are in this volume. If fully successful Clementine will return about 3 million lunar images, a dataset with nearly as many bits of data (uncompressed) as the first cycle of Magellan and more than 5000 images of Geographos. The complete and efficient analysis of such large data sets requires systematic processing efforts. Described below are concepts for two such efforts for the Clementine mission: global multispectral imaging of the Moon and videos of the Geographos flyby. Other anticipated datasets for which systematic processing might be desirable include multispectral observations of Earth; LIDAR altimetry of the Moon with high-resolution imaging along each ground track; high-resolution LIDAR color along each lunar ground track which could be used to identify potential titanium-rich deposits at scales of a few meters; and thermal IR imaging along each lunar ground track (including nighttime observations near the poles).

Mcewen, A. S.↗
NASA NTRS2016

3-D Flash Lidar Performance in Flight Testing on the Morpheus Autonomous, Rocket-Propelled Lander to a Lunar-Like Hazard Field

For the first time, a 3-D imaging Flash Lidar instrument has been used in flight to scan a lunar-like hazard field, build a 3-D Digital Elevation Map (DEM), identify a safe landing site, and, in concert with an experimental Guidance, Navigation, and Control (GN&C) system, help to guide the Morpheus autonomous, rocket-propelled, free-flying lander to that safe site on the hazard field. The flight tests served as the TRL 6 demo of the Autonomous Precision Landing and Hazard Detection and Avoidance Technology (ALHAT) system and included launch from NASA-Kennedy, a lunar-like descent trajectory from an altitude of 250m, and landing on a lunar-like hazard field of rocks, craters, hazardous slopes, and safe sites 400m down-range. The ALHAT project developed a system capable of enabling safe, precise crewed or robotic landings in challenging terrain on planetary bodies under any ambient lighting conditions. The Flash Lidar is a second generation, compact, real-time, air-cooled instrument. Based upon extensive on-ground characterization at flight ranges, the Flash Lidar was shown to be capable of imaging hazards from a slant range of 1 km with an 8 cm range precision and a range accuracy better than 35 cm, both at 1-delta. The Flash Lidar identified landing hazards as small as 30 cm from the maximum slant range which Morpheus could achieve (450 m); however, under certain wind conditions it was susceptible to scintillation arising from air heated by the rocket engine and to pre-triggering on a dust cloud created during launch and transported down-range by wind.

Roback, Vincent E.↗
NASA NTRS2010

The Slope Imaging Multi-Polarization Photon-Counting Lidar: Development and Performance Results

The Slope Imaging Multi-polarization Photon-counting Lidar is an airborne instrument developed to demonstrate laser altimetry measurement methods that will enable more efficient observations of topography and surface properties from space. The instrument was developed through the NASA Earth Science Technology Office Instrument Incubator Program with a focus on cryosphere remote sensing. The SIMPL transmitter is an 11 KHz, 1064 nm, plane-polarized micropulse laser transmitter that is frequency doubled to 532 nm and split into four push-broom beams. The receiver employs single-photon, polarimetric ranging at 532 and 1064 nm using Single Photon Counting Modules in order to achieve simultaneous sampling of surface elevation, slope, roughness and depolarizing scattering properties, the latter used to differentiate surface types. Data acquired over ice-covered Lake Erie in February, 2009 are documenting SIMPL s measurement performance and capabilities, demonstrating differentiation of open water and several ice cover types. ICESat-2 will employ several of the technologies advanced by SIMPL, including micropulse, single photon ranging in a multi-beam, push-broom configuration operating at 532 nm.

Dabney, Phillip↗
NASA NTRS2012

G-LiHT: Goddard's LiDAR, Hyperspectral and Thermal Airborne Imager

Scientists at NASA's Goddard Space Flight Center have developed an ultra-portable, low-cost, multi-sensor remote sensing system for studying the form and function of terrestrial ecosystems. G-LiHT integrates two LIDARs, a 905 nanometer single beam profiler and 1550 nm scanner, with a narrowband (1.5 nanometers) VNIR imaging spectrometer and a broadband (8-14 micrometers) thermal imager. The small footprint (approximately 12 centimeters) LIDAR data and approximately 1 meter ground resolution imagery are advantageous for high resolution applications such as the delineation of canopy crowns, characterization of canopy gaps, and the identification of sparse, low-stature vegetation, which is difficult to detect from space-based instruments and large-footprint LiDAR. The hyperspectral and thermal imagery can be used to characterize species composition, variations in biophysical variables (e.g., photosynthetic pigments), surface temperature, and responses to environmental stressors (e.g., heat, moisture loss). Additionally, the combination of LIDAR optical, and thermal data from G-LiHT is being used to assess forest health by sensing differences in foliage density, photosynthetic pigments, and transpiration. Low operating costs (approximately $1 ha) have allowed us to evaluate seasonal differences in LiDAR, passive optical and thermal data, which provides insight into year-round observations from space. Canopy characteristics and tree allometry (e.g., crown height:width, canopy:ground reflectance) derived from G-LiHT data are being used to generate realistic scenes for radiative transfer models, which in turn are being used to improve instrument design and ensure continuity between LiDAR instruments. G-LiHT has been installed and tested in aircraft with fuselage viewports and in a custom wing-mounted pod that allows G-LiHT to be flown on any Cessna 206, a common aircraft in use throughout the world. G-LiHT is currently being used for forest biomass and growth estimation in the CONUS and Mexico in support of NASA's Carbon Monitoring System (CMS) and AMIGA-Carb (AMerican Icesat Glas Assessment of Carbon). For NASA's CMS, wall-to-wall G-LiHT data have been acquired over intensive study sites with historic LiDAR datasets, dense inventory data, stem maps and flux tower observations. For AMIGA-Carb, G-LiHT transects have been acquired over ICESat tracks and USDA-FS inventory plots throughout the CONUS, and similar data will be acquired in Mexico during 2013. This talk will highlight recent science results from continental-scale transects landscape-scale deployments of G-LiHT, as well as seasonal forest dynamics from repeat pass G-LiHT acquisitions.

Cook, Bruce↗
DOE OSTI2021

Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain

We present a proof of concept of wind turbine wake identification and characterization using a region-based convolutional neural network (CNN) applied to lidar arc scan images taken at a wind farm in complex terrain. We show that the CNN successfully identifies and characterizes wakes in scans with varying resolutions and geometries, and can capture wake characteristics in spatially heterogeneous fields resulting from data quality control procedures and complex background flow fields. The geometry, spatial extent and locations of wakes and wake fragments exhibit close accord with results from visual inspection. The model exhibits a 95% success rate in identifying wakes when they are present in scans and characterizing their shape. To test model robustness to varying image quality, we reduced the scan density to half the original resolution through down-sampling range gates. This causes a reduction in skill, yet 92% of wakes are still successfully identified. When grouping scans by meteorological conditions and utilizing the CNN for wake characterization under full and half resolution, wake characteristics are consistent with a priori expectations for wake behavior in different inflow and stability conditions.

17 WIND ENERGY↗
DOE OSTI2020

Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu

Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Region-based convolutional neural network for wind turbine wake characterization from scanning lidars

A convolutional neural network is applied to lidar scan images from three experimental campaigns to identify and characterize wind turbine wakes. Initially developed as a proof-of-concept model and applied to a single data set in complex terrain, the model is now improved and generalized and applied to two other unique lidar data sets, one located near an escarpment and one located offshore. The model, initially developed using lidar scans collected in predominantly westerly flow, exhibits sensitivity to wind flow direction. The model is thus successfully generalized through implementing a standard rotation process to scan images before input into the convolutional neural network to ensure the flow is westerly. The sample size of lidar scans used to train the model is increased, and along with the generalization process, these changes to the model are shown to enhance accuracy and robustness when characterizing dissipating and asymmetric wakes. Applied to the offshore data set in which nearly 20 wind turbine wakes are included per scan, the improved model exhibits a 95% success rate in characterizing wakes and a 74% success rate in characterizing dissipating wake fragments. The improved model is shown to generalize well to the two new data sets, although an increase in wake characterization accuracy is offset by an increase in model sensitivity and false positive wake identifications.

17 WIND ENERGY↗
NASA NTRS2017

Evaluation of the Simple Safe Site Selection (S4) Hazard Detection Algorithm Using Helicopter Field Test Data

Small scale terrain hazards, such as rocks, slopes, and craters, can pose significant risk to landing spacecraft and rover or payload deployment. Onboard Hazard Detection and Avoidance (HDA) systems scan and analyze the landing area for these hazards in real time during descent, and divert the spacecraft to the safest touchdown site. The computationally efficient Simple Safe Site Selection (S4) algorithm combined with a flash LIDAR is an HDA system geared towards small robotic spacecraft. Rather than creating and analyzing a digital elevation map (DEM) from potentially many overlapping range images, S4 operates directly on a single flash LIDAR image. Extending prior work that has analyzed S4 performance for Mars landing using extensive simulations, this paper evaluates S4 performance using actual flash LIDAR images of an artificial hazard field acquired during a 2014 helicopter field test in Death Valley, CA. In particular, we describe LIDAR characterization and calibration, creation of ground truth elevation and safety maps, creation of ground truth sensor poses, actual S4 algorithm processing, and performance analysis. The results show that the safety cost images produced by S4 are remarkably close to the ground truth safety map (computed offline by an HDA algorithm developed under the Autonomous Landing and Hazard Avoidance (ALHAT) project) at significantly reduced computational cost, confirming S4 as a viable candidate algorithm for onboard spacecraft HDA.

Luna, Michael E.↗
NASA NTRS2013

A Comparison of Foliage Profiles in the Sierra National Forest Obtained with a Full-Waveform Under-Canopy EVI Lidar System with the Foliage Profiles Obtained with an Airborne Full-Waveform LVIS Lidar System

Foliage profiles retrieved froma scanning, terrestrial, near-infrared (1064 nm), full-waveformlidar, the Echidna Validation Instrument (EVI), agree well with those obtained from an airborne, near-infrared, full-waveform, large footprint lidar, the Lidar Vegetation Imaging Sensor (LVIS). We conducted trials at 5 plots within a conifer stand at Sierra National Forest in August, 2008. Foliage profiles retrieved from these two lidar systems are closely correlated (e.g., r = 0.987 at 100 mhorizontal distances) at large spatial coverage while they differ significantly at small spatial coverage, indicating the apparent scanning perspective effect on foliage profile retrievals. Alsowe noted the obvious effects of local topography on foliage profile retrievals, particularly on the topmost height retrievals. With a fine spatial resolution and a small beam size, terrestrial lidar systems complement the strengths of the airborne lidars by making a detailed characterization of the crowns from a small field site, and thereby serving as a validation tool and providing localized tuning information for future airborne and spaceborne lidar missions.

Full-waveform↗